"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:List[str]									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[str]									=			{
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Optional[Any]							  =						"""speech_to_text_2"""
 _snake_case       :					Optional[int]							  =						["""past_key_values"""]
 _snake_case       :					Optional[int]							  =						{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}


 def __init__( self    ,		lowerCamelCase=10000    ,		lowerCamelCase=6    ,		lowerCamelCase=2048    ,		lowerCamelCase=4    ,		lowerCamelCase=0.0    ,		lowerCamelCase=True    ,		lowerCamelCase="relu"    ,		lowerCamelCase=256    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.02    ,		lowerCamelCase=2    ,		lowerCamelCase=True    ,		lowerCamelCase=1    ,		lowerCamelCase=0    ,		lowerCamelCase=2    ,		lowerCamelCase=1024    ,		**lowerCamelCase    ,		):
        __a       =    vocab_size
        __a       =    d_model
        __a       =    decoder_ffn_dim
        __a       =    decoder_layers
        __a       =    decoder_attention_heads
        __a       =    dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    activation_function
        __a       =    init_std
        __a       =    decoder_layerdrop
        __a       =    use_cache
        __a       =    decoder_layers
        __a       =    scale_embedding  # scale factor will be sqrt(d_model) if True
        __a       =    max_target_positions

        super().__init__(
            pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase    ,		decoder_start_token_id=lowerCamelCase    ,		**lowerCamelCase    ,		)

"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]									=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__:Optional[int]									=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__:Optional[Any]									=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:int									=			[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	_lowerCamelCase(	a				):
       __a       =    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,    a				)
       return [m.group(0				) for m in matches]



def 	_lowerCamelCase(	):
       __a       =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a       =    {
           config.replace("Config"   ,    ""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(a				):
              __a       =    None
              if _re_tf_models.match(a				) is not None:
                     __a       =    tf_models
                     __a       =    _re_tf_models.match(a				).groups()[0]
              elif _re_flax_models.match(a				) is not None:
                     __a       =    flax_models
                     __a       =    _re_flax_models.match(a				).groups()[0]
              elif _re_pt_models.match(a				) is not None:
                     __a       =    pt_models
                     __a       =    _re_pt_models.match(a				).groups()[0]

              if lookup_dict is not None:
                     while len(a				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   __a       =    True
                                   break
                            # Try again after removing the last word in the name
                            __a       =    "".join(camel_case_split(a				)[:-1]				)

       __a       =    set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       __a       =    list(a				)
       all_models.sort()

       __a       =    {"model_type": all_models}
       __a       =    [pt_models[t] for t in all_models]
       __a       =    [tf_models[t] for t in all_models]
       __a       =    [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       __a       =    {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     __a       =    "AutoProcessor"
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     __a       =    "AutoTokenizer"
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     __a       =    "AutoFeatureExtractor"
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     __a       =    "AutoTokenizer"

       __a       =    [processors[t] for t in all_models]

       return pd.DataFrame(a				)



def 	_lowerCamelCase(	a				):
       __a       =    [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              __a       =    [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a       =    [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(a   ,    a   ,    a				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(a   ,    a				):
                            continue
                     # First extract all model_names
                     __a       =    []
                     for name in getattr(a   ,    a				).values():
                            if isinstance(a   ,    a				):
                                   model_names.append(a				)
                            else:
                                   model_names.extend(list(a				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    get_frameworks_table()
       __a       =    Dataset.from_pandas(a				)

       __a       =    hf_hub_download(
           "huggingface/transformers-metadata"   ,    "pipeline_tags.json"   ,    repo_type="dataset"   ,    token=a				)
       __a       =    Dataset.from_json(a				)
       __a       =    {
           tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
           for i in range(len(a				)				)
       }
       __a       =    update_pipeline_and_auto_class_table(a				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a       =    sorted(table.keys()				)
       __a       =    pd.DataFrame(
           {
               "model_class": model_classes,
               "pipeline_tag": [table[m][0] for m in model_classes],
               "auto_class": [table[m][1] for m in model_classes],
           }				)
       __a       =    Dataset.from_pandas(a				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(a   ,    "frameworks.json"				)				)
              tags_dataset.to_json(os.path.join(a   ,    "pipeline_tags.json"				)				)

              if commit_sha is not None:
                     __a       =    (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a       =    "Update"

              upload_folder(
                  repo_id="huggingface/transformers-metadata"   ,    folder_path=a   ,    repo_type="dataset"   ,    token=a   ,    commit_message=a   ,    )



def 	_lowerCamelCase(	):
       __a       =    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       __a       =    transformers_module.pipelines.SUPPORTED_TASKS
       __a       =    []
       for key in pipeline_tasks:
              if key not in in_table:
                     __a       =    pipeline_tasks[key]["pt"]
                     if isinstance(a   ,    (list, tuple)				):
                            __a       =    model[0]
                     __a       =    model.__name__
                     if model not in in_table.values():
                            missing.append(a				)

       if len(a				) > 0:
              __a       =    ", ".join(a				)
              raise ValueError(
                  "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                  F"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			argparse.ArgumentParser()
   parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
   parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
   parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)
